Using Big Data for Analytics Transformation

What does a Twitter feed tell you about your company? How about the number of clicks on a specific blog or group of websites? Both of these things may not be immediately useful to you, but it may hold the key to your future success. They make up Big Data – which means “information that’s too much for traditional software to analyze.” Big data can come in petabytes. It can hold immense value, and is the go-to tool in corporate analytics. [https://www.oracle.com/big-data/guide/what-is-big-data.html]

Analytics Transformation

Analytics transformation is leveraging data to provide insight, as part of a company’s strategy. It is imperative that transformation is across all lines of the business as the overall goal is to get key insights about what is going on in and outside the corporation.

History of Analytics

In the last decade or so, there has been a lot of transformation on how data is perceived or applied for use within organizations.

There are three stratified generations of data analytics, namely:

Analytics 1.0 --> Analytics 2.0 --> Analytics 3.0

In the Analytics 1.0 era, data was stored inside data servers, which were actually data warehousing centers. It was all about amassing all the data in one location to provide a single source of truth with some scheduled reports and ad-hoc reporting capabilities. Analysts and end users gleaned important insights from this stored data.

Analytics 2.0 was all about understanding and adding value to huge data sets, i.e., Big Data. This includes both includes structured and unstructured big data. Additionally, there was visualization with self-service reporting capabilities.

We are currently at the Analytics 3.0 era. This is a powerful and extremely far-reaching shift of analytics. It is all about obtaining, using and adding value to ever-increasing Big Data which is in petabytes of data and very specific information that can be gleaned from it. Moreover, there is increased emphasis on visualization and ad hoc reporting capabilities. To take it a notch higher, predictive analytics, artificial intelligence and machine learning play a very important role too. [https://hbr.org/2013/12/analytics-30]

In a few short years, there has been a rapid shift in technology in this field, just as much as in the other fields. Data is created everywhere. Some sources of data include:

• Corporate Applications (E-Commerce, Web sites, CRM, ERP, etc.)
• Machine and Sensor data
• Social Data, collected from a large pool of individuals using social platforms to create and share content.

Data can be created on company premises, public clouds, private clouds, via distributors and corporate partners. Due to this diversity, one single analytics platform or tool is no longer suitable across the corporation. It is imperative to pick the right platform or tool for each business unit. Supply chain, for example, might need an enterprise application. On the other hand, the Marketing arm of the organization might need a niche analytics tool.

Many corporations consider data analytics to be an integral, strategic arm. This is evident by the fact that there are roles like the Head of Data, Head of Analytics and Chief Data Officer. These officers report directly to CIO or CEO.

Why Data Analytics is Vital for Corporate Growth

With good data analytics, corporations can realize the following benefits:

• Insights into revenue, costs, products and more.
• The Life cycle of customer, including all touch points.
• Insights into internal organizations like Sales, Service, Operations, Fraud, Research, etc.
• Insights into external partners, distribution, the supply chain, etc.
• Identify hidden revenue opportunities.
• Understand spending across the organization.

Let us go through a use case, which has all three Vs (Velocity, Volume, and Variety). The goal is to ingest, transform and visually share the data in near real time. So, one has to look at the scale and frequency of data, type of enrichment is required, type of modeling (dimensions and aggregates) required. Identify the KPIs that provide insight into data and in relationship with real time data. 

Then one requires right UI design for the data. It is critical to plan for sub second response in Analytics 3.0 world. The number of users and concurrency should be taken into consideration.  The analytics application should provide in-depth and comprehensive insight into data and enable users (Analysts, Data Scientists, Managers and Executives) to make informed decisions based on the KPIs.

In my next blog, I will share about how to unlock Analytics transformation with effective roadmap.